In recent years, various deep learning based methods have been successfully developed for change detection, such as Convolutional Neural Network (CNN) based U-Net and its variants, and Transformer based ones. However, CNNs lack the ability to effectively learn global representations, while Transformers neglect to learn local representations. Therefore, in this paper we propose a novel deep network, namely Multi-scale Attention based Transformer U-Net (MATU), to take advantages of CNNs and Transformers for learning both local and global features effectively. The backbone of our proposed MATU is...
